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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
April 2018 | Vol. 96 No. 8

Title:	OFFLINE HANDWRITTEN SIGNATURE RECOGNITION USING HISTOGRAM ORIENTATION GRADIENT AND SUPPORT VECTOR MACHINE
Author:	NIDAA HASAN ABBAS, KHALED N. YASEN, KAMARAN HAMA ALI FARAJ, LWAY FAISAL A.RAZAK, FAHAD LAYTH MALALLAH
Abstract:	Human being authentication by offline handwritten signature biometric research has been increasing, especially in the last decade. Verification process of an offline handwritten signature is not trivial task, because an individual rarely signs exactly the same signature whenever he/she signs, which is referred to as intra-user variability. The objective of this paper is proposing a feature vector of an offline handwritten signature by using an efficient algorithm as a strong feature extraction namely Histogram Orientation Gradient (HOG), in order to be passed into Support Vector Machine (SVM) classifier for the recognition operation. An experiment has been conducted to estimate the accuracy and performance of the proposed algorithm by using SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals. The result has given accuracy as 96.8% as successful rate coming from the error type as: False Accept Rate (FAR) is 3% and False Reject Rate (FRR) is 3.35%.
Keywords:	Offline Signature, Biometrics, verification, Histogram Oriented Gradient (HOG), Support Vector Machine (SVM)
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	PEDESTRIAN USING CATADIOPTRIC SENSOR
Author:	BOUI MAROUANE, HADJ-ABDELKADER HICHAM, ABABSA FAKHR-EDDINE, ABOUYAKHF EL HOUSSINE
Abstract:	We investigate the detection of person in the omnidirectional images, adopting a linear SVM. We have implemented HOG-based descriptors, for omnidirectional and spherical images. In this paper we studied the influence of each parameter in our algorithm on the performances of person detections in catadioptric images. However, few studies have elaborated the problem of human detection using this type of cameras; therefore we have set up our own test base. Our results show that our detector can robustly detect people in omnidirectional images, as soon as the algorithm is adapted to the distortions introduced by the use of the omnidirectional camera.
Keywords:	Omnidirectional Sensor, HOG, Human Detection, Spherical Images Spherical Images Spherical Images.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	HUMAN FACTORS FOR IOT SERVICES UTILIZATION FOR HEALTH INFORMATION EXCHANGE
Author:	MOHAMMED AHMED DAUWED; JAMAIAH YAHAYA; ZULKEFLI MANSOR; ABDUL RAZAK HAMDAN
Abstract:	Currently, the exchange of patient information continues to be a challenge. The growing demand for health care makes it necessary to enhance the exchange of health-related information efficiently. IoT makes data available easily to exchange health-related information for health professionals’. Internet of Things (IoT) services can improve the quality of life and help health care professionals in their decision-making. Health records can be exchanged easily through the IoT network. The IoT is growing technology to integrate all smart devices, resources, and systems to discover drugs, treatments, and health records of patients in one network. Despite the advantages of this technology, there are a lot of challenges facing the healthcare organizations to utilize it especially in the context of developing countries. As such, the researchers carried out the literature survey for the related information in order to investigate the current issues and the factors that affect medical professionals and IT practitioners in order to use IoT in health information exchange. The purpose of this study is to help researchers and practitioners to develop the models for utilizing the IoT in health information exchange among healthcare providers. The researchers found the main critical factors to be, intention to use, user satisfaction, collaboration environment, trust, efforts, and service quality, which must be taken into consideration. From the review, a number of critical factors were found to be essential in the utilization of IoT.
Keywords:	Internet of Things; Health Information Exchange, Healthcare System; Utilization IoT; Human Factors.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	A NOVEL ALGORITHM TO REDUCE PEAK-TO-AVERAGE POWER RATIO OF ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING SIGNALS
Author:	MANZOOR AHMED HASHMANI, FARZANA RAUF ABRO, MUKHTIAR ALI UNAR, JUNZO WATADA
Abstract:	It has been observed over last several decades that bandwidth greediness of applications never gets fulfilled. Hence, scientists, researchers, and engineers keep working on new ways of providing higher bandwidth. Recently, a new modulation technique called Orthogonal Frequency Division Multiplexing (OFDM) has been introduced which provides very high data rates. In OFDM the high frequency input signal is modulated over a large number of low frequency sub-carrier signals which are orthogonal to each other. This feature makes it very robust against efficiency degradation at higher frequencies. That is the reason why OFDM is a choice for the modern high and ultra-high data rate communication systems. However, it suffers from high levels of the peak power to the average power also called Peak-to-Average Power Ratio or PAPR. Reducing PAPR in OFDM is a hot research area. There are many schemes available which attempt to reduce PAPR. Some are in fact able to reduce PAPR but not sufficient enough to make these feasible. Others do reduce it but increase its complexity to an extent that these become unfeasible to realize. From literature it has been identified that SLM performs better than other methods in terms of computational complexity at the same performance level. The main motivation behind this research effort is to find a mechanism which reduces PAPR for OFDM systems and has a reasonable level of complexity so that it may be realizable. As an outcome of this research activity, a novel framework based on Artificial Neural Networks (ANN) and Selective Mapping (SLM) is proposed. The kernel used by the ANN in proposed framework is a modified version (proposed by us) of an already available kernel called Novel Kernel Based – Radial Basis Function (NKB-RBF). We show through simulations results that our proposed kernel, Modified NKB-RBF (MNKB-RBF), is more efficient than NKB-RBF and gives better results in selection of low frequency sub-carriers with lowest PAPR.
Keywords:	Orthogonal Frequency Division Multiplexing (OFDM), Peak-to-Average Power Ratio (PAPR), Selective Mapping (SLM), Artificial Neural Networks (ANN), Radial Basis Function (RBF)
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	INTRUSION DETECTION SYSTEM USING BOOTSTRAP RESAMPLING APPROACH OF T2 CONTROL CHART BASED ON SUCCESSIVE DIFFERENCE COVARIANCE MATRIX
Author:	MUHAMMAD AHSAN, MUHAMMAD MASHURI, HIDAYATUL KHUSNA
Abstract:	The multivariate control chart is one of SPC method that is often used in intrusion detection. The Hotelling’s T2 control chart with Successive Difference Covariance Matrix (SDCM) is the robust method that can detect outliers in the process data for individual observation. This method will effective to be applied in Intrusion Detection System (IDS) because it can detect the anomaly or outliers in the network. The problem arise when the exact distribution of this method has not determined. Bootstrap is one of the nonparametric method that widely used to estimate the parameter without any distribution assumption applied to overcome the problem. In this research, the Hotelling’s T2 control chart is improved using the SDCM while its control limits is calculated using bootstrap resampling method. The proposed method is applied in IDS and its performance is compared to the other control chart approaches. The performance evaluation result shows that the proposed IDS with bootstrap control limit performs better than the other control chart approaches for testing dataset. Moreover, the proposed IDS outperforms the other classification methods.
Keywords:	T2 Control Chart, Successive Difference Covariance Matrix, Kernel Density Estimation, Bootstrap, Intrusion Detection System
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	MACHINE LEARNING CONFIGURATIONS FOR HUMAN PROTEIN CLASSIFICATION USING SDFES
Author:	SUNNY SHARMA , AMRITPAL SINGH , GURVINDER SINGH , RAJINDER SINGH
Abstract:	The identification of target proteins for diseased condition yields the development of the disease detection recommender system and drug discovery processes whose reticence can demolish the pathogen. The testing of this drug discovery is done through clinical and in addition through pre-clinical observations first on the creatures then on people. Thereafter the discovered drug is ready for public use. But if the drug discovery testing phase does not show the suitable consequences, then the entire task must be repeated. This repetitive clinical as well as the preclinical experimentation task is very cumbersome. But keeping in view the importance of the disease detection and drug discovery phase in protein identification as well as in the protein classification process this task must be done by researchers. The advancements in computational biology reveal the importance of computational prediction of protein function or to identify the target on the basis of protein sequence extracted features. To accurately predict the human protein functionalities, lots of approaches are incorporated but this is a very cumbersome task due to the large and versatile nature of the domain. The present work will help to do this job through computational prediction. This paper involves the development of a model which use associative rule mining to extract the sequence derived features at a single platform (SDFES-Sequence derived feature extraction server) from the given human protein sequence and then critically analyzed with machine learning (ML) approaches under the aegis of data analysis tool WEKA. The new sequence derived features are identified and incorporated in the data set, and the scopes of ML approaches were examined for effective prediction. The important configuration incorporation and their configured comparison of approaches are completed to accomplish higher accuracy. In addition to comparative analysis, the limitation of ML approach is discussed along with its remedies by changing the configurations. The proposed work will assist to derive the sequence extracted feature together at a single place and further predict the class or function of the protein which leads to the innovation in drug discovery and disease detection recommender systems.
Keywords:	Protein, Machine Learning, WEKA, Random Forest, Decision Tree.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	AODV CLUSTERING ALGORITHM BASED ON DENSITY AND NODES MOBILITY WITH A MOBILE REFERENCE
Author:	M. SAADOUNE, M. DYABI, A. HAJAMI, H. ALLALI
Abstract:	A mobile ad hoc network (MANET) is generally defined as a network that has many free nodes, often composed of mobile devices, thus the mobility of the nodes effects a much the performance of the network. AODV is a routing protocol for mobile ad hoc networks (MANET), it has low processing and memory overhead and low network utilization, and works well even in high mobility situation, but it has issues for large mobile networks. A clustering architecture provides network scalability and fault tolerance, and results in more efficient use of network resources. For those reasons, we propose in this paper two kind of clustering algorithm in AODV: Density-based algorithm and mobility-based algorithm using a mobile reference. Our objective is to elect a reduced number of less mobile cluster heads.
Keywords:	Ad hoc, Mobility, Localization, Distance, Relative speed, AODV, Metric, Clustering
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	COMBINATION MATHEMATICAL DISTANCE MEASURE APPROACH FOR SOME IMAGE PROCESSING APPLICATIONS
Author:	SHAHAD ADIL TAHER, HIND RUSTUM MOHAMMED
Abstract:	In this paper the researcher presents a Combination mathematical Distance Measure approach for Image Encryption; it had been applied on different images . The aim of this method to protect the images by encryption them. The proposed method is depending on find a new distance between image pixels by using interpolation. The study data base consists of (40 images) of different types (color images and gray images) with different formats(. jpg, .bmp, .tif , .gif, . png ). The Proposed method gave more accurate results, stronger encoding of images and high encryption efficiency via using the performance evaluation factors to evaluate labor standards and proved the successfulness of this high encryption ratios method of. MATLAB® 2010 software had been highly relied in this study.
Keywords:	Image Processing, Image Encryption, Distance Measure, Performance Analyses Image
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	SYSTEM OF DECISION SUPPORT IN WEAKLY-FORMALIZED PROBLEMS OF TRANSPORT CYBERSECURITY ENSURING
Author:	AKHMETOV B., LAKHNO V.
Abstract:	This paper resolves the actual task of the development of mathematical software decision support systems (DSS) cyber security mission-critical information systems of transport (CRIST) in poorly structured and difficult tasks of formalizing the information security and information risk assessment. The paper presents developed system for decision support in weakly formalized problems of CRIST and the cyber security of objects of Informatization of the industry. The system is based on models of information security tasks description, risk assessment and cyber defense of transport in conceptual and functional aspects. Also the article presents the description of the process of forming the DSS knowledge base for circumstances related to the identification of hard-to-explain signs of anomalies and attacks.
Keywords:	System Of Support Of Decision-Making, Cyber Security, Poorly Formalized Tasks, The Interpretation Of The Situation.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	THE IDENTIFICATION OF DATA ANOMALIES FROM INFORMATION SENSORS BASED ON THE ESTIMATION OF THE CORRELATION DIMENSION OF THE TIME SERIES ATTRACTOR IN SITUATIONAL MANAGEMENT SYSTEMS
Author:	FARIZA BILYALOVNA TEBUEVA , VLADIMIR VYACHESLAVOVICH KOPYTOV , VIACHESLAV IVANOVICH PETRENKO , ANDREY OLEGOVICH SHULGIN, NIKITA GEORGIEVICH DEMIRTCHEV5
Abstract:	Purpose: The goal is the timely detection of uncharacteristic behavior of the observed processes in the systems of situational management, leading to the development or occurrence of emergency situations. Methodological approach: In the article, it is proposed to analyze the change dynamics in the correlation dimension of the attractor in order to detect anomalies in the behavior of the observed process. A sharp change in the correlation dimension is a reflection of the uncharacteristic (anomalous) nature of the data of the observed processes. This anomaly is a consequence of external influences on the generating system and requires an analysis of the causes of its occurrence. Uniqueness/value: The uniqueness of the proposed approach consists in the fact that an abrupt change in the correlation dimension of the attractor is the information about the occurrence of uncharacteristic behavior of the observed system. The value of the study is determined by the relevance of the problem of modeling the development and occurrence of emergency situations in situational management systems based on the analysis of the time series of observed processes. Summary: The proposed approach is designed to identify the critical states of the generating dynamical systems by their time series. Timely response to the transition of the monitored system to a critical state will allow preventing any critical consequences.
Keywords:	Correlation dimension, Attractor, Anomalous behavior, Data of information sensors.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	WHALE OPTIMIZATION ALGORITHM FOR SOLVING THE MAXIMUM FLOW PROBLEM
Author:	RAJA MASADEH , ABDULLAH ALZAQEBAH , AHMAD SHARIEH
Abstract:	Maximum Flow Problem (MFP) is deemed as one of several well-known basic problems in weighted direct graphs [9]. Moreover, it can be applied to many applications in computer engineering and computer science. This problem is solved by many techniques. Thus, this study presents a possible solution to the max flow problem (MFP) using a Whale Optimization algorithm (WOA), which is considered as one of the most recent optimization algorithms that was suggested in 2016. The experimental results of the “MaxFlow-WO” algorithm that were tested on various datasets are good evidence that the s technique can solve the MFP and reinforce its performance. It aims to solve the MFP by clustering the search space to find the MF for each cluster (local MF) in order to find the overall solution (global MF) of the desired graph. The WOA is used to solve the MFP by supposing the graph is the search space that the whales looking to reach the prey. Here, the prey is the sink in the network (represented by the graph) and other whales are represented in other nodes in the graph.
Keywords:	Whale Optimization, Maximum Flow Problem, Maximum Flow, Meta-Heuristic, Optimization
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	EVALUATING THE INFLUENCE OF FEATURE SELECTION TECHNIQUES ON MULTI-LABEL TEXT CLASSIFICATION METHODS USING MEKA
Author:	SUSAN KOSHY, DR. R.PADMAJAVALLI
Abstract:	Multi-label classification has generated a lot of interest with its useful applications in real world situations as against traditional single label classification. Feature selection has a positive affect on the performance of multi-label classification as it elevates the performance of learning algorithms, reducing the storage requirement and complexity of the multidimensional space. There are many multi label algorithms to handle the problem of multi-label classification and the issue of dimensionality is overcome by feature selection. There are several feature selection techniques and the right combination of multi-label classification and multi-label feature selection will help in building an efficient model of classification for a given dataset. This paper uses the available algorithms and evaluates the influence of filter feature selection methods and multi-label classification for two standard text datasets drawn from real domains. Multi-label Problem transformation transform the multi-label dataset into single label. Binary Relevance, Classifier Chains, Pruned Sets and an ensemble method called RAkEL, multi-label classifiers with two single label classifier namely J48 and Naïve base are used. Feature selection is followed by multi-label classification. This paper uses five standard techniques namely correlation feature subset selection, correlation feature selection, gain ratio, information gain and ReliefF to evaluate the relationship between feature selection, multi-label classification and single label base classifier in order to obtain enhanced multi-label evaluation metrics
Keywords:	Correlation Based Feature Subset Selection, Correlation Feature Selection, Multi-Label Text Classification, Gain Ratio, Information Gain, Relieff.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	SEMANTIC ROLE LABELING OF MALAYALAM WEB DOCUMENTS IN CRICKET DOMAIN
Author:	SUNITHA C, DR. A JAYA, AMAL GANESH
Abstract:	Document Summarization is an ongoing research work in the field of Natural Language Processing which will provide a summary which is almost like a summary generated by a human being with the help of NLP tools and techniques. Since the information used across the digital world is exponentially increasing, automatic summarization techniques gained attention especially abstractive methods. But producing an effective abstractive summary, first, the text documents should be represented semantically. From this representation, important sentences must be selected using some strategies and finally the abstractive summary is generated. Representing the sentences in natural language semantically faces many challenges. Various works have been carried out for extracting the semantics of the sentences. Semantic role labeling is a technique in NLP to detect the semantically related arguments of a predicate or verb in a sentence and their grouping into one of the related roles. So this technique can be used to represent the sentences meaningfully and can be further used in different applications such as question answering system, information extraction, summarization, text categorization etc. Currently, limited works are done in Malayalam towards semantic role extraction. Domain based works will give better results. In this paper the semantic roles of important words in Malayalam Web documents pertaining to cricket domain are identified.
Keywords:	Semantic Role Labeling, Karaka relations, Memory Based Learning, Vibhakthi, Chunking
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	SFT: A MODEL FOR SENTIMENT CLASSIFICATION USING SUPERVISED METHODS ON TWITTER
Author:	RAZIEH ASGARNEZHAD, S. AMIRHASSAN MONADJEMI, MOHAMMADREZA SOLTANAGHAEI, AYOUB BAGHERI
Abstract:	Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Thousand of millions of people around the world intensity use web sites such as Twitter. Twitter, as a micro-blogging system, allows users to publish tweets to tell others what they are thinking. In fact, there are already many web sites built on the Internet providing a Twitter sentiment search service. In those web sites, the user can input a sentiment target and in searching for tweets containing positive and negative sentiments. As a result of the increasing number of tweets over the past few years, tweets have attracted more and more attention. This is striking for consumers to research the sentiment of products before purchase automatically. This paper proposes a novel model for Twitter Sentiment Classification. The purpose of this model is investigating what is the role of weighting feature techniques in Sentiment Classification using supervised methods on the Twitter data set. Also, it explores binary classification which is classified data set into positive and negative classes. It is shown that usage of the proposed model can improve 7% the accuracy of Twitter Sentiment Classification. The results confirmed the superiority of the proposed model over the state-of-the-art systems.
Keywords:	Sentiment Classification, Support Vector Machine, Supervised Method, Twitter
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	MULTI OBJECTIVE INTEGRATED CROSSOVER BASED PARTICLE SWARM OPTIMIZATION FOR LOAD BALANCING IN CLOUD DATA CENTERS
Author:	NEHA SETHI, SURJIT SINGH, GURVINDER SINGH
Abstract:	Cloud computing is becoming popular day by day, due to its wide range of scalable and dynamic characteristics. The increase in number of cloud users leads to the imbalance in resources and cloud data centers utilization and drastically improves the energy consumption. Therefore, it increases the data centers cost and waiting time of cloud users. Therefore, improving the waiting time and optimum usage of cloud resources has become a challenging issue. Many approaches have been designed to balance the user loads between cloud data centers. However, majority of existing load balancing approaches suffer from pre-mature convergence issue, stuck in local optima issue and poor convergence issue. To overcome this issue, in this paper a multi-objective integrated crossover based particle swarm optimization has been proposed for balancing the load between cloud data centers. The proposed technique has been designed and implemented in MATLAB 2013a tool with the help of parallel processing toolbox Extensive analysis reveal that the proposed technique outperforms existing techniques in terms of average waiting time, makes pan and degree of imbalance. Analysis of Variance (ANOVA) based statistical testing has also been utilized to evaluate the significant improvement of the proposed technique.
Keywords:	Particle Swarm Optimization, Meta heuristics, Cloud data centers, Load balancing, Analysis of Variance.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	TRANSMISSION QUALITY AWARE OPTIMUM CHANNEL SCHEDULING (QAOCS) FOR 802.11A WLAN
Author:	MACHA SARADA, DR. AVULA DAMODARAM
Abstract:	WLAN networks have emerged as the most deployed last stage component of internet connectivity to mobile users. Growing subscribers of WLANs for Wi-Fi accessing devices is driving the traffic in WLANs. Accordingly, WLAN services are prone to severe declines in performance amid channel interference and contention. To address rising traffic issues and performance degradation, a novel channel scheduling strategy is proposed. Unlike the most of the contemporary models, the proposed model schedules the channels based on multi-objective QoS factors, moreover it balances the load by transmitting buffered data packets as transmission-window. The contention state of channel availability also addressed in this proposal. The research work depicts that the recommended algorithm signifies the improved throughput, defused drop ratio, and also distributes the user traffic based on optimizing channel scheduling.
Keywords:	WLAN, 802.11a, 802.11ac, Channel scheduling, Access point, OCA-ITU.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	AN APPROACH FOR DETECTING SYNTAX AND SYNTACTIC AMBIGUITY IN SOFTWARE REQUIREMENT SPECIFICATION
Author:	ALI OLOW JIM’ALE SABRIYE, WAN MOHD NAZMEE WAN ZAINON
Abstract:	Software requirements are considered to be ambiguous if the requirements statement could have more than one interpretation. The ambiguous requirements could cause the software developers to develop software which is different from what the customer needs. The focus of this paper is to propose an approach to detect syntax and syntactic ambiguity in software requirements specification. In this paper, Parts of speech (POS) tagging technique has been used to detect these ambiguities. A prototype tool has been developed in order to evaluate the proposed approach. The evaluation is done by comparing the detection capabilities of the proposed tool against human capabilities. The overall results show that the humans do have some difficulties in detecting ambiguity in software requirements, especially the syntactic ambiguity and software requirements that contains both syntax and syntactic ambiguity in one sentence. The proposed tool can definitely help the analyst in detecting ambiguity in Software requirements.
Keywords:	Part of speech tagging, Syntax ambiguity, Syntactic ambiguity, Software requirements specification.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	AN EFFICIENT ARTIFICIAL FISH SWARM ALGORITHM WITH HARMONY SEARCH FOR SCHEDULING IN FLEXIBLE JOB-SHOP PROBLEM
Author:	ISHRAQ F. FAEQ, MEHDI G. DUAIMI, AHMED T. SADIQ AL-OBAIDI
Abstract:	Flexible job-shop scheduling problem (FJSP) is one of the instances in flexible manufacturing systems. It is considered as a very complex to control. Hence generating a control system for this problem domain is difficult. FJSP inherits the job-shop scheduling problem characteristics. It has an additional decision level to the sequencing one which allows the operations to be processed on any machine among a set of available machines at a facility. In this article, we present Artificial Fish Swarm Algorithm with Harmony Search for solving the flexible job shop scheduling problem. It is based on the new harmony improvised from results obtained by artificial fish swarm algorithm. This improvised solution is sent to comparison to an overall best solution. When it is the better one, it replaces with the artificial fish swarm solution from which this solution was improvised. Meanwhile the best improvised solutions are carried over to the Harmony Memory. The objective is to minimize a total completion time (makespan) and to make the proposed approach as a portion of the expert and the intelligent scheduling system for remanufacturing decision support. Harmony search algorithm has demonstrated to be efficient, simple and strong optimization algorithm. The ability of exploration in any optimization algorithm is one of the key points. The obtained optimization results show that the proposed algorithm provides better exploitation ability and enjoys fast convergence to the optimum solution. As well, comparisons with the original artificial fish swarm algorithm demonstrate improved efficiency.
Keywords:	Artificial Fish Swarm Algorithm; Harmony Search; Makespan; Flexible Job-Shop Scheduling Problem.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	IMAGE PREPROCESSING OF ABDOMINAL CT SCAN TO IMPROVE VISIBILITY OF ANY LESIONS IN KIDNEYS
Author:	HIMA BINDU G, PRASAD REDDY PVGD, M. RAMAKRISHNA MURTY
Abstract:	Abdominal CT scan images are widely used in detection of kidney lesions. This paper study is conducted for pre-processing abdominal CT scan images so as to segment the kidney for further analysis of lesion detection. Various noise filters and segmentation techniques have been experimented to select the best filter and segmentation techniques for pre-processing the CT image. The experimental study finds a combination of Median filter followed by Wiener filter more effective to remove different noises present in the CT images. Different segmentation techniques have been run on the test data set of CT images and it is observed that Edge based active contour produced better results than Graph Cut and region based active contours.
Keywords:	Medical Imaging, Noise Filters, Segmentation, Active Contour, Region-based, Edge-based, Graph Cut.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	IMAGE TAMPER DETECTION AND RECOVERY USING LIFTING SCHEME-BASED FRAGILE WATERMARKING
Author:	TAHA BASHEER TAHA, RUZELITA NGADIRAN, PHAKLEN EHKAN, , MOHAMAD T. SULTAN
Abstract:	High prevalence of digital images imposed a great interest in the process of authority and integrity investigation. In many cases, high accuracy image tamper detection techniques involves with high computational overhead in addition to the complexity produced from recovering original image process. In this work, blind image tamper detection and self-recovery is presented using Lifting Scheme which characterized by simplicity and integer based calculations and LSB modification. Experimental results reveal that proposed model performs well in terms of detection and recovery for different types of tampering as removing and cloning. Furthermore, produced watermarked images have very accepted perceptual quality in terms of subjective and objective evaluations.
Keywords:	Lifting Scheme, LWT, Image Tamper Detection, Image Recovery, Fragile Watermarking.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	A CHAOS WITH DISCRETE MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR PAVEMENT MAINTENANCE
Author:	KAWTHER AHMED , BELAL AL-KHATEEB , MAHER MAHMOOD
Abstract:	Particle Swarm Optimization (PSO) is a very popular technique in swarm intelligence. PSO has been applied to solve many problems that have single or multi-objectives. In fact, the multi-objectives optimization problems in real life are combinatorial in nature. Therefore, PSO has been developed to be able to handle large number of decision variables and reduce computational complexity. In this paper, a chaos multi objective PSO algorithm is developed for solving discrete (binary) optimization problems. The developed algorithm is applied to pavement management problem to find optimal maintenance and rehabilitation plan for flexible pavement with maximum pavement conditions and minimum maintenance cost. The results show that there is significant improvement in the solutions satisfying pavement conditions and maintenance cost objectives. It is required to a very short time of execution by the developed algorithm to reach a very good solution. In addition, it is found that it is able to converge to the solution faster than another PSO algorithm.
Keywords:	Multi-Objective Optimization, Pavement Maintenance, Particle Swarm Optimization, Chaotic Mapping, Binary PSO.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	ARABIC NEWS CREDIBILITY ON TWITTER: AN ENHANCED MODEL USING HYBRID FEATURES
Author:	SAHAR F. SABBEH , SUMAIA Y. BAATWAH
Abstract:	Recently, social media and specially Twitter has become a main source for news consumption and sharing among millions of users. Those platforms enable users to author, publish and share content. Such environments can be used to publish and spread rumors and fake news whether unintentionally or even maliciously. That is why credibility of information in such platforms has been increasingly investigated in many domains (i.e. information sciences, psychology, sociology...etc). This paper proposes a machine learning - based model for Arabic news credibility assessment on Twitter. It uses hybrid set of features that are topic and user related to evaluate news credibility. In addition to the traditional content-related features, Content verifiability and users' replies polarity analysis used for a more accurate assessment. The proposed model consists of four main modules: a) content parsing and features extraction module, b) content verification module, c) users’ comments polarity evaluation and d) credibility classification module. A data set of 800 Arabic news that are manually labeled is collected from Twitter. Three different classification techniques were applied (Decision tree, support vector machine (SVM) and Naive Bayesian(NB). For model training and testing, 5-fold cross validations were performed and performance diagnostics were calculated. Results indicate that decision tree achieves TRP higher than SVM by around 2% and 7% than NB, also FPR almost 9% lower than SVM and 10% lower than NB. For precision,recall, f-measure and accuracy, decision tree achieves almost 2% higher than SVM and 7% higher than NB for the tested data-set. Experiments also revealed that the proposed system achieves accuracy that outperforms the system proposed by Hend.et.al [29] and TweetCred [2].
Keywords:	News Credibility, Arabic News, Machine Learning, Twitter, Verifiability, Text Polarity
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	SYSTEM COUPLING AND COHESION REQUIREMENTS MODEL (SC2RM): MEASUREMENT APPROACH FOR REAL TIME SYSTEM
Author:	KHALED ALMAKADMEH
Abstract:	One of the main challenges for software development organizations is to build software systems with measured complexity. Monitoring a software system complexity help software engineers in development phases of system development life cycle, such as software system reusability and software system maintainability. A key measure of software complexity is the degree of cohesion and coupling within and between its components. The literature emphasizes that a key system element to measure the degree of cohesion and coupling is the number of interactions between software components. This paper propose a new model to measure the degree of cohesion and coupling within and between real-time system components based on ISO19761 international standard. A case study is conducted to verify the applicability of the proposed measurement model using structural specifications and First Class Relation. The resulting measures are valuable indicators of a software system complexity that directly affects its reusability and maintainability.
Keywords:	Coupling; Cohesion; Software Measurement; Real-time System, ISO19761.
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	COMPUTER-BASED PORTFOLIO ASSESSMENT TO ENHANCE STUDENTS’ SELF-REGULATED LEARNING
Author:	GUSTI AYU MAHAYUKTI, NYOMAN DANTES, 3I MADE CANDIASA, ANAK AGUNG ISTRI NGURAH MARHAENI, I NYOMAN GITA, DEWA GEDE HENDRA DIVAYANA
Abstract:	The aim of this study is to elaborate the impact of computer-based portfolio assessment to improve the students’ self-regulated learning. The population of this research was the students of Mathematics Education Department, Universitas Pendidikan Ganesha, who followed the Integral Calculus course in academic year 2015/2016. From those, 88 students were chosen randomly as the sample. Pretest-Posttest Control Group Design was employed as the method of the present study. The data were gathered from a self-regulated learning questionnaire with 19 positive and 18 negative statements. The collected data were analyze using descriptive and inferential techniques using t-test. The result showed that there is an improvement of the students’ self regulated learning after following the course with computer-based portfolio assessment. The finding implies that when using computer-based portfolio assessment in the classroom, it is better in team teaching format.
Keywords:	Computer-Based Portfolio Assessment, Self-Regulated Learning
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	NON-CONVEX ECONOMIC LOAD DISPATCH PROBLEMS USING NOVEL BAT ALGORITHM
Author:	HARDIANSYAH
Abstract:	In this paper a novel bat algorithm (NBA) is proposed for solving non-convex economic load dispatch (ELD) problems so as to minimize the total generation cost when considering the linear and non linear constraints. The proposed algorithm combines the bats’ habitat selection and their self-adaptive compensation for Doppler effects in echoes into the basic bat algorithm (BA). The selection of bats’ habitat is modeled as the selection between their quantum behaviors and mechanical behaviors. Many nonlinear characteristics of the power generators and practical constraints, such as power loss, ramp rate limits, prohibited operating zones and valve-point effects, are considered. The effectiveness and feasibility of the proposed method are demonstrated by two real power systems and compared with other optimization algorithms reported in literature.
Keywords:	Novel Bat Algorithm, Non-Convex Economic Load Dispatch, Ramp Rate Limits, Prohibited Operating Zones, Valve-Point Effects
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
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Title:	AN EFFICIENT DIGITAL VIDEO WATERMARKING SYSTEM ROBUST AGAINST VARIOUS SPATIAL, TEMPORAL, and SPATIO-TEMPORAL ATTACKS
Author:	MANISH K THAKUR
Abstract:	For many years information hiding techniques are playing major roles in achieving robustness against various malicious attacks on digital multimedia data of different application areas. The induction of digital watermarking has given these practices a new way in the field of information hiding to prevent the copyrighted multimedia data against various attacks. In this paper, we propose a generalised watermarking system which efficiently identifies various attacks in spatial, temporal, and spatio-temporal domain, viz. frame deletion, frame swapping, and frame copying, etc. Based on the timing information of each frame of a video, in the proposed scheme we first generate the unique watermarks (representing the timing information) in real time and then embed the auto generated watermark into 8 x 8 discrete cosine transform blocks of corresponding frames of a video. While extracting the watermarks from each frame of the watermarked video, any alterations in the sequence of the retrieved watermarks are the indication of the temporal attacks (frame deletion, etc.) in the copyrighted videos. Experiments have been conducted on self captured videos to analyze the performance of the proposed watermarking model. Performance of the proposed model has been measured in terms of the recall rate, i.e. ability of the proposed watermarking model to correctly detect the attacks in videos, quality of the watermarked videos and extracted watermarks using the quality metrics, Peak Signal to Noise Ratio (PSNR). Experimental results show the 100% recall rate in detection of the temporal attacks, if videos are manipulated in temporal domain only. Achieved recall rate is in between the range 86% and 91%, if videos are manipulated in spatio-temporal domain and it is 93%, if videos are spatially manipulated.
Keywords:	Watermarking, Spatio-temporal attacks, Frame deletion, Frame swapping, Frame Copying
Source:	Journal of Theoretical and Applied Information Technology 30^th April 2018 -- Vol. 96. No. 8 -- 2018
Full Text